Model save path: ./New_Models/bn_False_dataset_CIFAR100_epochs_200_lr_0.01_model_type_vgg11_rand_seed_979323_test_samples_None_train_samples_None_weight_decay_0.001.pth.tar
Training Set:
Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022893190383911133
Inter Cos: 0.07011394202709198
Norm Quadratic Average: 29.326318740844727
Nearest Class Center Accuracy: 0.03258

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.037905994802713394
Inter Cos: 0.06100593879818916
Norm Quadratic Average: 35.83223342895508
Nearest Class Center Accuracy: 0.04424

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.04327956959605217
Inter Cos: 0.04230519011616707
Norm Quadratic Average: 45.38289260864258
Nearest Class Center Accuracy: 0.05236

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03855273872613907
Inter Cos: 0.03961573541164398
Norm Quadratic Average: 77.38819122314453
Nearest Class Center Accuracy: 0.0617

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03553709760308266
Inter Cos: 0.03327590227127075
Norm Quadratic Average: 50.550209045410156
Nearest Class Center Accuracy: 0.07036

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.034686919301748276
Inter Cos: 0.03072742186486721
Norm Quadratic Average: 29.25092315673828
Nearest Class Center Accuracy: 0.07536

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.07954678684473038
Inter Cos: 0.05532452464103699
Norm Quadratic Average: 9.682600975036621
Nearest Class Center Accuracy: 0.08484

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.35663047432899475
Inter Cos: 0.20118515193462372
Norm Quadratic Average: 4.710749626159668
Nearest Class Center Accuracy: 0.09912

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 7.176231861114502
Linear Weight Rank: 4024
Intra Cos: 0.647006630897522
Inter Cos: 0.33806973695755005
Norm Quadratic Average: 28.271846771240234
Nearest Class Center Accuracy: 0.09996

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 7.1198954582214355
Linear Weight Rank: 3591
Intra Cos: 0.6796609163284302
Inter Cos: 0.3292011022567749
Norm Quadratic Average: 35.441993713378906
Nearest Class Center Accuracy: 0.1

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 7.230754852294922
Linear Weight Rank: 98
Intra Cos: 0.6847033500671387
Inter Cos: 0.3185757100582123
Norm Quadratic Average: 43.60440444946289
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.7098252773284912
Inter Cos: 0.36404553055763245
Norm Quadratic Average: 64.75995635986328
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 3.5908452674865723
Accuracy: 0.4697
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.237775981426239, Weights: 0.03414785489439964
NC2 Equiangle: Features: 0.23630119554924242, Weights: 0.12671184501262625
NC3 Self-Duality: 0.467184454202652
NC4 NCC Mismatch: 0.264

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.006621268577873707
Inter Cos: 0.4067547619342804
Norm Quadratic Average: 29.42218780517578
Nearest Class Center Accuracy: 0.1136

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.016146402806043625
Inter Cos: 0.30835771560668945
Norm Quadratic Average: 36.03458023071289
Nearest Class Center Accuracy: 0.2212

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.023899808526039124
Inter Cos: 0.3378949463367462
Norm Quadratic Average: 45.65275955200195
Nearest Class Center Accuracy: 0.2753

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.024218767881393433
Inter Cos: 0.3067837059497833
Norm Quadratic Average: 77.9566879272461
Nearest Class Center Accuracy: 0.3434

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.024516534060239792
Inter Cos: 0.24524593353271484
Norm Quadratic Average: 51.026554107666016
Nearest Class Center Accuracy: 0.4441

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.02132302150130272
Inter Cos: 0.17668963968753815
Norm Quadratic Average: 29.45382308959961
Nearest Class Center Accuracy: 0.5055

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.03228329122066498
Inter Cos: 0.2603178918361664
Norm Quadratic Average: 9.665825843811035
Nearest Class Center Accuracy: 0.5123

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.09152153134346008
Inter Cos: 0.5068197846412659
Norm Quadratic Average: 4.568238735198975
Nearest Class Center Accuracy: 0.5042

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 7.176231861114502
Linear Weight Rank: 4024
Intra Cos: 0.13428327441215515
Inter Cos: 0.6067273020744324
Norm Quadratic Average: 26.384782791137695
Nearest Class Center Accuracy: 0.4741

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 7.1198954582214355
Linear Weight Rank: 3591
Intra Cos: 0.13782665133476257
Inter Cos: 0.6147577166557312
Norm Quadratic Average: 33.02198791503906
Nearest Class Center Accuracy: 0.471

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 7.230754852294922
Linear Weight Rank: 98
Intra Cos: 0.13543419539928436
Inter Cos: 0.6128566861152649
Norm Quadratic Average: 40.87382125854492
Nearest Class Center Accuracy: 0.4684

Output Layer:
Intra Cos: 0.1378011405467987
Inter Cos: 0.66059410572052
Norm Quadratic Average: 60.78810501098633
Nearest Class Center Accuracy: 0.4589

